CN102612712B - Bandwidth extension of low band audio signal - Google Patents

Abstract

Estimation of a high band extension of a low band audio signal includes the following steps: extracting (S1) a set of features of the low band audio signal; mapping (S2) extracted features to at least one high band parameter with generalized additive modeling; frequency shifting (S3) a copy of the low band audio signal into the high band; controlling (S4) the envelope of the frequency shifted copy of the low band audio signal by said at least one high band parameter.

Description

The bandwidth expansion of low band audio signal

Technical field

The present invention relates to audio coding, more specifically, relate to the bandwidth expansion of low band audio signal.

Background technology

The present invention relates to the bandwidth expansion (BWE) of sound signal.In voice and audio coding/decoding, by BWE scheme, improve the perceived quality under given bit rate more and more.BWE based on main theory be: do not send a part of sound signal, but according to the component of signal that receives, rebuild (estimation) this part sound signal at demoder place.

Therefore, in BWE scheme, in a part for demoder place reconstruction signal frequency spectrum.With utilizing the special characteristic of the signal spectrum of the actual transmission of traditional coding method, carry out this reconstruction.Conventionally, according to specific low-frequency band (LB) audio signal characteristic, carry out the high frequency band (HB) of reconstruction signal.

Conventionally by gauss hybrid models (GMM) or Hidden Markov Model (HMM) (HMM), the dependence between LB feature and HB characteristics of signals is carried out to modeling (for example, [1-2]).The most often the HB characteristic of prediction is relevant with spectrum envelope and/or temporal envelope.

There is the BWE scheme of two kinds of main Types:

● in the first scheme, according to specific LB feature, predict HB characteristics of signals completely.

These BWE solutions have been introduced pseudo-sound (artifact) in the HB rebuilding, and this causes the quality of the reduction compared with bandwidth limited signal in some cases.Complex mappings (for example,, based on GMM or HMM) easily causes the deteriorated of unknown data.

General experience is: shine upon more complicated (number of training parameter is larger), for non-existent data type in training set, occur that the possibility of pseudo-sound is just higher.The mapping of finding the optimum balance that will be given between whole precision of prediction and a small amount of abnormal data (outlier, obviously departs from the data of the data in training set, can not by the component of fine modeling) to have complexity is very difficult.

● alternative plan (example of describing in [3]) is to rebuild HB signal according to the combination of LB feature and a small amount of HB information sending.Utilize the BWE scheme of the HB information sending to tend to improve performance (cost is the bit budget increasing), but the conventional method that the parameter of the parameter of transmission and prediction is combined is not provided.Conventionally, send a set of HB parameter, and another set of prediction HB parameter, it means the fault of sent information in can not the parameter of compensation prediction.

Summary of the invention

The object of the invention is to realize improved BWE scheme.

According to appended claim, realized this object.

According to first aspect, the present invention relates to estimate the method for the high frequency band expansion of low band audio signal.The method comprises the following steps.Extract the characteristic set of low band audio signal.Utilize Generalized Additive Models (generalized additive modeling) that the Feature Mapping of extracting is arrived at least one high frequency band parameters.The copy frequency displacement of low band audio signal is arrived to high frequency band.By described at least one high frequency band parameters, control the envelope of copy after the frequency displacement of low band audio signal.

According to second aspect, the present invention relates to for estimating the device of the high frequency band expansion of low band audio signal.Feature extraction piece is configured to extract the characteristic set of low band audio signal.Mapping block comprises following unit: Generalized Additive Models mapper, is configured to utilize Generalized Additive Models that the Feature Mapping of extracting is arrived at least one high frequency band parameters; Frequency shifter, is configured to the copy frequency displacement of low band audio signal to high frequency band; Envelope control device, is configured to control by described at least one high frequency band parameters the envelope of copy after frequency displacement.

According to the third aspect, the present invention relates to comprise the Voice decoder according to the device of second aspect.

According to fourth aspect, the present invention relates to comprise the network node according to the Voice decoder of the third aspect.

The advantage of the BWE scheme proposing is: it provides good balance between complex mappings scheme (good average behavior, but a large amount of abnormal datas) and the mapping scheme of multiple constraint more (lower average behavior, but more robust).

Accompanying drawing explanation

Carry out in conjunction with the drawings reference description below, can understand best the present invention and other objects and advantage, in the accompanying drawings:

Fig. 1 shows the block diagram of the embodiment that comprises that the coding/decoding of Voice decoder is arranged according to an embodiment of the invention;

Fig. 2 A-C shows the figure of the principle of Generalized Additive Models;

Fig. 3 shows according to of the present invention for generating the block diagram of embodiment of the device of HB expansion;

Fig. 4 shows the figure of the example of the high frequency band parameters obtaining by Generalized Additive Models according to an embodiment of the invention;

Fig. 5 shows the figure of definition of the feature of applicable extraction according to another embodiment of the present invention;

Fig. 6 shows the block diagram of embodiment that is applicable to generating based on feature shown in Fig. 5 the device of HB expansion according to of the present invention;

Fig. 7 shows the diagram of the example of the high frequency band parameters that the feature based on shown in Fig. 5 obtains by Generalized Additive Models according to an embodiment of the invention;

Fig. 8 shows the block diagram of another embodiment of the coding/decoding layout that comprises Voice decoder according to another embodiment of the present invention;

Fig. 9 shows the block diagram of the another embodiment of the coding/decoding layout that comprises Voice decoder according to still another embodiment of the invention;

Figure 10 shows according to of the present invention for generating the block diagram of another embodiment of the device of HB expansion;

Figure 11 shows according to of the present invention for generating the block diagram of another embodiment of the device of HB expansion;

Figure 12 shows the block diagram comprising according to the embodiment of the network node of the embodiment of Voice decoder of the present invention;

Figure 13 shows according to the block diagram of the embodiment of Voice decoder of the present invention;

Figure 14 shows the process flow diagram of the embodiment of the method according to this invention.

Embodiment

In the accompanying drawings, to thering is the unit of same or similar function, provide identical invoking marks.

Hereinafter, explain LB characteristic set and by mapping, carried out the usage of the HB part of estimated signal.In addition, also explained that how the HB information sending is can be for controlling mapping.

Fig. 1 shows the block diagram of the embodiment that comprises that the coding/decoding of Voice decoder is arranged according to an embodiment of the invention.Speech coder 1 reception sources sound signal s (conventionally receiving its frame), is transmitted to analysis filterbank 10, and analysis filterbank 10 is divided into low-frequency band part s by sound signal _lBwith highband part s _hB.In this embodiment, HB is partly dropped (it means that analysis filterbank can only comprise low-pass filter).The LB part s of sound signal _lBfor example, in LB scrambler 12 (normally Code Excited Linear Prediction (CELP) scrambler, Algebraic Code Excited Linear Prediction (ACELP) scrambler), encode, and code is sent to Voice decoder 2.In [4], can find the example of ACELP coding/decoding.The code that Voice decoder 2 receives for example, is decoded in LB demoder 14 (normally CELP demoder, ACELP demoder), and LB demoder 14 provides and s _lBcorresponding low band audio signal

this low band audio signal

be forwarded to feature extraction piece 16, feature extraction piece 16 extracts signal

feature F _lBset (described below).The feature F extracting _lBbe forwarded to mapping block 18, mapping block 18 utilizes Generalized Additive Models (described below) by the feature F extracting _lBbe mapped at least one high frequency band parameters (described below).HB parameter is used to control LB sound signal

frequency displacement to the envelope of the copy of high frequency band, wherein this envelope provides the HB part s to abandoning _hBestimation

signal with

be forwarded to synthesis filter banks 20, the estimation that synthesis filter banks 20 is rebuild original source sound signal feature extraction piece 16 is formed for generating the device 30 (below further describing) of HB expansion together with mapping block 18.

The exemplary L B audio signal characteristic (being called local feature) of below introducing is used to predict specific HB characteristics of signals.Can use all features or subset in exemplified feature.Calculate frame by frame all these local features, local feature dynamically also comprises from the information of frame before.Hereinafter, n is frame index, and l is sample index, and s (n, l) is speech samples.

Two exemplary characteristics are with spectral tilt and tilt dynamically relevant.They measure the frequency distribution of energy:

${\mathrm{\Ψ}}_1\left(n\right)=\frac{\underset{l=1}{\overset{L}{\mathrm{\Σ}}}s(n,l)s(n,l-1)}{\underset{l=1}{\overset{L}{\mathrm{\Σ}}}{s}^2(n,l)}---\left(1\right)$

${\mathrm{\Ψ}}_2\left(n\right)=\frac{|{\mathrm{\Ψ}}_1\left(n\right)-{\mathrm{\Ψ}}_1(n-1)|}{{\mathrm{\Ψ}}_1\left(n\right)+{\mathrm{\Ψ}}_1(n-1)}---\left(2\right)$

Ensuing two exemplary characteristics measurement pitch (voice basic frequency) and pitch are dynamic.Pass through τ _mINand τ _mAXsearch for optimal delay is limited in to significant pitch range, for example 50-400Hz:

${\mathrm{\&Psi;}}_3\left(n\right)=\underset{{\mathrm{\&tau;}}_{\text{MIN}}<\mathrm{\&tau;}<{\mathrm{\&tau;}}_{\mathrm{MAX}}}{\arg \max }\frac{\underset{l=1}{\overset{L}{\mathrm{\&Sigma;}}}s(n,l)s(n,l+\mathrm{\&tau;})}{\sqrt{\underset{l=1}{\overset{L}{\mathrm{\&Sigma;}}}{s}^2(n,l)\underset{l=1}{\overset{L}{\mathrm{\&Sigma;}}}{s}^2(n,l+\mathrm{\&tau;})}}---\left(3\right)$

${\mathrm{\&Psi;}}_4\left(n\right)=\frac{|{\mathrm{\&Psi;}}_3\left(n\right)-{\mathrm{\&Psi;}}_3(n-1)|}{{\mathrm{\&Psi;}}_3\left(n\right)+{\mathrm{\&Psi;}}_3(n-1)}---\left(4\right)$

The the 5th and the 6th exemplary characteristics has reflected tonal components in signal and the balance between noise like component.Herein,

with

for example, self-adaptation in CELP encoding and decoding (ACELP encoding and decoding) and the energy of fixed codebook, and

the energy of pumping signal:

${\mathrm{\&Psi;}}_5\left(n\right)=\frac{{\mathrm{\&sigma;}}_{\mathrm{ACB}}^2\left(n\right)-{\mathrm{\&sigma;}}_{\mathrm{FCB}}^2\left(n\right)}{{\mathrm{\&sigma;}}_e^2\left(n\right)}---\left(5\right)$

${\mathrm{\&Psi;}}_6\left(n\right)=\frac{|{\mathrm{\&Psi;}}_5\left(n\right)-{\mathrm{\&Psi;}}_5(n-1)|}{{\mathrm{\&Psi;}}_5\left(n\right)+{\mathrm{\&Psi;}}_5(n-1)}---\left(6\right)$

Last local feature frame by frame in this example collection catches energy dynamics.Herein, the energy of speech frame:

${\mathrm{\&Psi;}}_7\left(n\right)=\frac{|\mathrm{lo}{g}_{10}\left({\mathrm{\&sigma;}}_s^2\left(n\right)\right)-\mathrm{lo}{g}_{10}\left({\mathrm{\&sigma;}}_s^2(n-1)\right)|}{\mathrm{lo}{g}_{10}\left({\mathrm{\&sigma;}}_s^2\left(n\right)\right)+\mathrm{lo}{g}_{10}\left({\mathrm{\&sigma;}}_s^2(n-1)\right)}---\left(7\right)$

All these local features that use in mapping carried out following convergent-divergent before mapping:

$\widetilde{\mathrm{\&Psi;}}\left(n\right)=\frac{\mathrm{\&Psi;}\left(n\right)-{\mathrm{\&Psi;}}_{\mathrm{MIN}}}{{\text{\&Psi;}}_{\mathrm{MAX}}-{\mathrm{\&Psi;}}_{\mathrm{MIN}}}---\left(8\right)$

Ψ wherein _mINand Ψ _mAXthe predetermined constant corresponding with the minimum value of given feature and maximal value.This has provided the characteristic set extracting

According to the present invention, according to local feature, estimate that HB expansion is based on Generalized Additive Models.For this reason, with reference to Fig. 2 A-C, this concept is briefly described.Can for example in [5], find the further details about Generalized Additive Models.

In statistics, often with regression model, carry out the behavior of estimated parameter.A kind of naive model is linear model:

$\hat{Y}={\mathrm{\&omega;}}_0+\underset{m=1}{\overset{M}{\mathrm{\&Sigma;}}}{\mathrm{\&omega;}}_m{X}_m---\left(9\right)$

Wherein

to depending on (at random) variable X ₁..., X _mthe estimation of variable Y.Its situation when M=2 has been shown in Fig. 2 A.In this case,

it will be flat surfaces.

The property feature of linear model be with each be only linearly dependent on a variable.The popularization of this feature is that these linear functions (at least one) are modified as to nonlinear function (it remains each and only depends on a variable).This causes additive model:

$\hat{Y}={\mathrm{\&omega;}}_0+\underset{m=1}{\overset{M}{\mathrm{\&Sigma;}}}{f}_m\left(X_m\right)---\left(10\right).$

The situation of this additive model when M=2 has been shown in Fig. 2 B.In this case, representative

surface be crooked.Function f _m(X _m) S shape (sigmoid) function (being generally serpentine function) typically, as shown in Figure 2 B.The example of sigmoid function is logarithmic function, Compertz curve, anti-arc (ogee) curve and hyperbolic tangent function.By change, define the parameter of sigmoid function, S shape shape can continuously change the approximate step function between identical minimum value and maximal value with the approximately linear shape between maximal value from minimum value.

By Generalized Additive Models acquisition below, further promote:

$g\left(\hat{Y}\right)={\mathrm{\&omega;}}_0+\underset{m=1}{\overset{M}{\mathrm{\&Sigma;}}}{f}_m\left(X_m\right)---\left(11\right)$

Wherein g () is called as link (link) function.This function has been shown in Fig. 2 C, wherein, surface

further revised (by equation (11) both sides being got to contrary g ^-1(), obtains

g wherein ^-1() is also sigmoid function conventionally).Link function g () be identity function in particular cases, equation (11) is simplified to equation (10).Because both of these case is all very important, for purposes of the present invention, " Generalized Additive Models " also will comprise the situation of identical link function.Yet, as mentioned above, at least one f _m(X _m) be nonlinear, it makes model is nonlinear (surface

crooked).

In an embodiment of the present invention, 7 (normalized) features that obtain according to equation (1) to (8)

be used to estimate HB energy in compression (perception promotes) territory and the ratio Y (n) between LB energy.This ratio can be corresponding with the specific part of temporal envelope or spectrum envelope, or corresponding with entire gain, below will be described further.An example is as follows:

$Y\left(n\right)={\left(\frac{E_{\mathrm{HB}}\left(m\right)}{E_{\mathrm{LB}}\left(n\right)}\right)}^{\mathrm{\&beta;}}---\left(12\right)$

Wherein β for example can be chosen as β=0.2.Another example is as follows:

$Y\left(n\right)=\mathrm{lo}{g}_{10}\left(\frac{E_{\mathrm{HB}}\left(n\right)}{E_{\mathrm{LB}}\left(n\right)}\right)---\left(13\right)$

In equation (12) and (13), parameter beta and log ₁₀function is for converting energy Ratios in " perception promotes " territory of compression.Carry out the susceptibility characteristic that this changes to consider the approximate logarithm of people's ear.

Because in demoder place ENERGY E _hB(n) unavailable, so prediction or estimation ratio Y (n).This is by LB feature and the estimation of Generalized Additive Models to Y (n) based on extracting

carrying out modeling completes.An example provides as follows:

$\hat{Y}\left(n\right){\mathrm{\&omega;}}_0+\underset{m=1}{\overset{M}{\mathrm{\&Sigma;}}}\left(\frac{w_{1\mathrm{<figure-callout\; id="1"\; label="m"\; filenames="HDA00001655406700011.png"\; state="\{\{state\}\}">m</figure-callout>}}}{1+e^{-w_{2m}{\widetilde{\mathrm{\&Psi;}}}_m\left(n\right)+w_{3m}}}\right)---\left(14\right)$

Wherein, M=7, and the local feature of given extraction (less feature is also feasible).Compare with equation (11), obviously

with variable X ₁..., X _pcorrespondence, and function f _kwith with in corresponding, it is by model parameter

sigmoid function with identical link function definition.This Generalized Additive Models parameter ω ₀be stored in demoder with ω, and by training to obtain on the database of speech frame.This training process is by minimizing the ratio of being estimated by equation (14)

and the error between the effective rate Y (n) being provided by equation (12) (or (13)) finds suitable parameter ω ₀and ω.A kind of suitable method (especially for S shape parameter) is Levenberg-Marquardt method of for example describing in [6].

Fig. 3 shows according to of the present invention for generating the block diagram of embodiment of the device 30 of HB expansion.Device 30 comprises feature extraction piece 16, and it is configured to extract the characteristic set of low band audio signal

mapping block 18, is connected to feature extraction piece 16, comprises Generalized Additive Models mapper 32, its be configured to utilize Generalized Additive Models by the Feature Mapping of extracting to high frequency band parameters in the embodiment shown, at mapping block 18, comprise and being configured to low band audio signal

copy frequency displacement to the frequency shifter 34 of high frequency band.In the embodiment shown, mapping block 18 also comprises envelope control device 36, and it is configured to pass through high frequency band parameters control the envelope of copy after frequency displacement.

Fig. 4 shows the figure of the example of the high frequency band parameters obtaining by Generalized Additive Models according to an embodiment of the invention.It shows the ratio (gain) that how to use estimation

control the envelope (being in this case in frequency domain) of copy after the frequency displacement of LB signal.Dotted line represents the no gain (1.0) of change of LB signal.Therefore, in this embodiment, by by single estimated gain after being applied to the frequency displacement of LB signal, copy obtains HB expansion.

Fig. 5 shows the figure of definition of the feature of applicable extraction according to another embodiment of the present invention.This embodiment only extracts 2 LB signal characteristic F ₁, F ₂.

In the embodiment shown in fig. 5, feature F1 is defined as follows:

${\mathrm{<figure-callout\; id="1"\; label="F"\; filenames="HDA00001655406700011.png"\; state="\{\{state\}\}">F</figure-callout>}}_1=\frac{E_{10.0-11.6}}{E_{8.0-11.6}}---\left(15\right)$

Wherein,

E _10.0-11.6the estimation of the energy in frequency band 10.0-11.6kHz to low band audio signal,

E _8.0-11.6it is the estimation of the energy in frequency band 8.0-11.6kHz to low band audio signal.

In addition, in the embodiment shown in fig. 5, feature F ₂be defined as follows:

${\mathrm{<figure-callout\; id="2"\; label="F"\; filenames="HDA00001655406700091.png"\; state="\{\{state\}\}">F</figure-callout>}}_2=\frac{E_{8.0-11.6}}{E_{0.0-11.6}}---\left(16\right)$

Wherein,

E _8.0-11.6the estimation of the energy in frequency band 8.0-11.6kHz to low band audio signal,

E _0.0-11.6it is the estimation of the energy in frequency band 0.0-11.6kHz to low band audio signal.

Feature F ₁, F ₂represent spectral tilt, and with feature above

similar, but they are determined at frequency domain rather than in time domain.In addition, on other frequency intervals of LB signal, determine feature F ₁, F ₂feasible.Yet in this embodiment of the present invention, main points are F ₁, F ₂energy Ratios between the different piece of low band audio signal frequency spectrum has been described.

Use the feature F extracting ₁, F ₂, mapper 32 now can be by using following Generalized Additive Models that they are mapped to HB parameter

${\hat{E}}_k=w_{0k}+\underset{m=1}{\overset{2}{\mathrm{\&Sigma;}}}\frac{{\mathrm{<figure-callout\; id="1"\; label="w"\; filenames="HDA00001655406700011.png"\; state="\{\{state\}\}">w</figure-callout>}}_{1\mathrm{<figure-callout\; id="1"\; label="mk"\; filenames="HDA00001655406700011.png"\; state="\{\{state\}\}">mk</figure-callout>}}}{1+\exp (-{\mathrm{<figure-callout\; id="2"\; label="w"\; filenames="HDA00001655406700091.png"\; state="\{\{state\}\}">w</figure-callout>}}_{2\mathrm{mk}}{F}_m+w_{3\mathrm{mk}})}---\left(17\right)$

Wherein,

k=1 ..., K is the high frequency band parameters that defines gain, the envelope of K predetermined frequency band of copy after the frequency displacement of this gain control low band audio signal,

{ w _0k, w _1mk, w _2mk, w _3mkfor each high frequency band parameters

the mapping coefficient set of definition sigmoid function,

F _m, m=1,2, be the feature of describing the low band audio signal of the energy Ratios between the different piece of low band audio signal frequency spectrum.

Fig. 6 shows the block diagram of embodiment that is applicable to generating based on feature shown in Fig. 5 the device of HB expansion according to of the present invention.This embodiment comprises the similar unit of embodiment with Fig. 3, but in this embodiment, they are configured to feature F ₁, F ₂be mapped to K gain rather than single gain

Fig. 7 shows the figure of the example of the high frequency band parameters obtaining based on feature shown in Fig. 5 by Generalized Additive Models according to an embodiment of the invention.In this example, there is K=4 gain these four gains

the envelope of 4 predetermined frequency bands of copy after the frequency displacement of control low band audio signal.Therefore, in this example, by 4 parameters

control HB envelope, rather than use single parameter as in the example with reference to figure 4

control HB envelope.Still less also feasible with more parameter.

Fig. 8 shows the block diagram of another embodiment of the coding/decoding layout that comprises demoder according to another embodiment of the present invention.The difference of the embodiment of this embodiment and Fig. 1 is: do not abandon HB signal s _hB.On the contrary, HB signal is forwarded to HB message block 22, and 22 pairs of HB signals of HB message block are classified, and sends N bit classification index to Voice decoder 2.If allow to send HB information (as shown in Figure 8), bunch (cluster) that mapping utilizes transmission to provide comes segmentation to carry out, and the number of wherein classifying depends on the amount of available bits.As mentioned below, category index is used by mapping block 18.

Fig. 9 shows the block diagram of the another embodiment of the coding/decoding layout that comprises demoder according to still another embodiment of the invention.The embodiment of this embodiment and Fig. 8 is similar, but uses HB signal s _hBand LB signal s _lBcome together to form category index.In this example, N=1 bit, but by comprising more bits, can also have more than 2 classifications.

Figure 10 shows according to of the present invention for generating the block diagram of another embodiment of the device of HB expansion.The difference of the embodiment of this embodiment and Fig. 3 is: it comprises mapping coefficient selector switch 38, and the signal category index C that mapping coefficient selector switch 38 is configured to based on receiving selects mapping coefficient set

in this embodiment, according to the set of low-frequency band feature

mapping coefficient ω with pre-stored ^cpredict high frequency band parameters

category index C selects the set of mapping coefficient, and the set of described mapping coefficient is determined by the off-line training process of the data in order in matching bunch.Can be regarded as state (without classification) from pure prediction HB seamlessly transitting to pure quantification HB state (having classification).The latter is the result of the following fact: in the situation that bunch number increase, mapping will trend towards the mean value of prediction bunch.

Figure 11 shows according to of the present invention for generating the block diagram of another embodiment of the device of HB expansion.The embodiment of this embodiment and Figure 10 is similar, but this embodiment is the feature F based on describing with reference to figure 5 ₁, F ₂.In addition, in this embodiment, by following classification (also with reference to figure 5 top), provide signal classification C:

Wherein,

the estimation of the energy in frequency band 8.0-11.6kHz to source sound signal,

it is the estimation of the energy in frequency band 11.6-16.0kHz to source sound signal.

In this example, C classifies (say roughly, provide the psychological picture of the represented content of this example classification) to sound, is divided into " voice " (classifying 1) and " non-voice " (classifying 2).

Based on this classification, mapping block 18 can be configured to carry out mapping according to following formula (Generalized Additive Models 32):

${\hat{E}}_k^C=w_{0k}^C+\underset{m=1}{\overset{2}{\mathrm{\&Sigma;}}}\frac{{\mathrm{<figure-callout\; id="1"\; label="w"\; filenames="HDA00001655406700011.png"\; state="\{\{state\}\}">w</figure-callout>}}_{1\mathrm{<figure-callout\; id="1"\; label="mk\; C"\; filenames="HDA00001655406700011.png"\; state="\{\{state\}\}">mk</figure-callout>}}^C}{1+\exp (-{\mathrm{<figure-callout\; id="2"\; label="w"\; filenames="HDA00001655406700091.png"\; state="\{\{state\}\}">w</figure-callout>}}_{2\mathrm{mk}}^C{F}_m+w_{3\mathrm{mk}}^C)}$

Wherein,

k=1 ..., K is high frequency band parameters, the gain that the definition of this high frequency band parameters is associated with signal classification C, and control the envelope of K predetermined frequency band of copy after the frequency displacement of low band audio signal, wherein said signal is classified C to by low band audio signal

the source sound signal representing is classified,

be in signal classification C, for each high frequency band parameters

the mapping coefficient set of definition sigmoid function,

F _m, m=1,2, be the feature of describing the low band audio signal of the energy Ratios between the different piece of low band audio signal frequency spectrum.

As example, K=4 and can define F by (15) and (16) ₁, F ₂.

The advantage of the embodiment of Fig. 8-11 is that they have realized feature from extracting to " fine setting " of the mapping of the type of the sound of having encoded.

Figure 12 shows the block diagram comprising according to the embodiment of the network node of the embodiment of Voice decoder 2 of the present invention.This embodiment shows wireless terminal, but other network nodes are also feasible.For example, if use the voice based on IP (Internet protocol) in network, node can comprise computing machine.

In the network node of Figure 12, the voice signal that antenna reception has been encoded.Detuner and channel decoder 50 convert this signal to low-frequency band speech parameter (signal classification C, as indicated in passed through (classification C) and void signal wire alternatively), and they are transmitted to Voice decoder 2, to generate voice signal

as reference, each embodiment is described above.

Step described herein, function, process and/or piece can be realized with the hardware with any conventional art, and described conventional art is for example discrete circuit or integrated circuit technique, comprise universal circuit and special circuit.

Alternatively, at least some in step described herein, function, process and/or piece can use the software of being carried out by suitable treatment facility to realize, described treatment facility is for example microprocessor, digital signal processor (DSP) and/or any suitable programmable logic device (PLD), as field programmable gate array (FPGA) device.

Be also to be understood that the common treatment ability of reusing network node is possible.This can be for example by realizing to existing software reprogramming or by adding new component software.

As a realization example, Figure 13 is the block diagram illustrating according to the example embodiment of Voice decoder 2 of the present invention.This embodiment is based on processor 100 (as microprocessor), its execution: component software 110, and for estimating low-frequency band voice signal

component software 120, for estimating high frequency band voice signal

and component software 130, for basis with

generate voice signal

this software is stored in storer 150.Processor 100 is by system bus and memory communication.By controlling with I/O (I/O) controller 160 of processor 100 and the storage 150 I/O buses that are connected, receive low-frequency band speech parameter (signal classification C alternatively).In this embodiment, the parameter that I/O controller 150 receives is stored in storer 150, and wherein they are processed by component software.Component software 110 can be realized the function of the piece 14 in above-described embodiment.Component software 120 can be realized the function of the piece 30 in above-described embodiment.Component software 130 can be realized the function of the piece 20 in above-described embodiment.By I/O controller 160, by I/O bus, from storer 150 outputs, be obtained from the voice signal of component software 130.

In the embodiment of Figure 13, by I/O controller 160 reception speech parameters, and hypothesis is by other local other tasks of processing in receiving network node, as the solution mediation channel-decoding in wireless terminal.Yet alternatives is to allow other component softwares in storer 150 also process for extract all or part of the digital signal processing of speech parameter from receiving signal.In such embodiments, can directly from storer 150, retrieve speech parameter.

In the situation that receive network node, be the computing machine that receives the voice based on IP grouping, IP grouping is forwarded to I/O controller 160 conventionally, and another component software in storer 150 extracts speech parameter.

Some or all assemblies in above-described component software can for example, carry on computer-readable medium (CD, DVD or hard disk), and loaded into memory is carried out for processor.

Figure 14 shows the process flow diagram of the embodiment of the method according to this invention.Step S1 extracts the characteristic set of low band audio signal

step S2 utilizes Generalized Additive Models that the Feature Mapping of extracting is arrived at least one high frequency band parameters

step S3 is by low band audio signal

copy frequency displacement to high frequency band.Step S4 controls the envelope of copy after the frequency displacement of low band audio signal by high frequency band parameters.

It will be understood by those skilled in the art that in the situation that do not depart from the scope being defined by the following claims of the present invention, can carry out various modifications and change to the present invention.

Abbreviation

ACELP Algebraic Code Excited Linear Prediction

BWE bandwidth expansion

CELP Code Excited Linear Prediction

DSP digital signal processor

FPGA field programmable gate array

GMM gauss hybrid models

HB high frequency band

HMM Hidden Markov Model (HMM)

IP Internet protocol

LB low-frequency band

List of references

[1]M.Nilsson and W.B.Kleijn，“Avoiding over-estimation in bandwidth extension of telephony speech”，Proc.IEEE Int.Conf.Acoust.Speech Sign.Process.，2001.

[2]P.Jax and P.Vary，“Wideband extension of telephone speech using a hidden Markov model”，IEEE Workshop on Speech Coding，2000.

[3]ITU-T Rec.G.729.1，“G.729-based embedded variable bit-rate coder：An 8-32kbit/s scalable wideband coder bitstream interoperable with G.729”，2006.

[4]3GPP TS 26.190，“Adaptive Multi-Rate-Wideband (AMR-WB)speech codec；Transcoding functions”，2008.

[5]“New Approaches to Regression by Generalized Additive Models and Continuous Optimizationfor Modern Applications in Finance，Science and Technology”，Pakize Taylan，Gerhard-Wilhelm Weber，Amir Beck， http://www3.iam.metu.edu.tr/iam/images/1/10/Preprint56.pdf

[6]Numerical Recipes in C++：The Art of Scientific Computing，2nd edition，reprinted 2003，W. Press，S.Teukolsky，W.Vetterling，B.Flannery

Claims (19)

1. estimate low band audio signal for one kind

high frequency band expansion

method, comprise the characteristic set of extraction (S1) low band audio signal step, described method is characterised in that:

Utilize Generalized Additive Models, extracted Feature Mapping (S2) is arrived at least one high frequency band parameters

By low band audio signal copy frequency displacement (S3) to high frequency band;

By described at least one high frequency band parameters, control the envelope of copy after the frequency displacement of (S4) low band audio signal.

2. method according to claim 1, wherein, the feature of described mapping based on extracted

sigmoid function and.

3. method according to claim 2, wherein, described mapping provides by following formula:

${\hat{E}}_k=w_{0k}+\underset{m=1}{\overset{2}{\mathrm{\&Sigma;}}}\frac{w_{1\mathrm{mk}}}{1+\exp (-w_{2\mathrm{mk}}{F}_m+w_{3\mathrm{mk}})}$

Wherein,

the high frequency band parameters that defines gain, the envelope of K predetermined frequency band of copy after the frequency displacement of described gain control low band audio signal,

{ w _0k, w _1mk, w _2mk, w _3mkthat definition is for each high frequency band parameters

the mapping coefficient set of sigmoid function,

F _m, m=1,2, be the feature of describing the low band audio signal of the energy Ratios between the different piece of low band audio signal frequency spectrum.

4. method according to claim 2, wherein, described mapping provides by following formula:

${\hat{E}}_k^C=w_{0k}^C+\underset{m=1}{\overset{2}{\mathrm{\&Sigma;}}}\frac{w_{1\mathrm{mk}}^C}{1+\exp (-w_{2\mathrm{mk}}^C{F}_m+w_{3\mathrm{mk}}^C)}$

Wherein,

high frequency band parameters, the gain that the definition of this high frequency band parameters is associated with signal classification C, and control the envelope of K predetermined frequency band of copy after the frequency displacement of low band audio signal, wherein, described signal is classified C to by low band audio signal

the source sound signal representing is classified,

that definition is for each high frequency band parameters in signal classification C

the mapping coefficient set of sigmoid function,

F _m, m=1,2, be the feature of describing the low band audio signal of the energy Ratios between the different piece of low band audio signal frequency spectrum.

5. according to the method described in claim 3 or 4, wherein, described feature F ₁by following formula, provide:

$F_1=\frac{E_{10.0-11.6}}{E_{8.0-11.6}}$

Wherein,

E _10.0-11.6the estimation of the energy in frequency band 10.0-11.6kHz to low band audio signal,

E _8.0-11.6it is the estimation of the energy in frequency band 8.0-11.6kHz to low band audio signal.

6. according to the method described in claim 3 or 4, wherein, described feature F ₂by following formula, provide:

$F_2=\frac{E_{8.0-11.6}}{E_{0.0-11.6}}$

Wherein,

E _8.0-11.6the estimation of the energy in frequency band 8.0-11.6kHz to low band audio signal,

E _0.0-11.6it is the estimation of the energy in frequency band 0.0-11.6kHz to low band audio signal.

7. according to the method described in claim 3 or 4, wherein, K=4.

8. method according to claim 4, comprises the following steps: select the mapping coefficient set corresponding with signal classification C

wherein, C is provided by following formula:

Wherein,

the estimation of the energy in frequency band 8.0-11.6kHz to source sound signal, and

it is the estimation of the energy in frequency band 11.6-16.0kHz to source sound signal.

9. one kind for estimating low band audio signal

high frequency band expansion

equipment (30), comprise the characteristic set that is configured to extract low band audio signal

feature extraction piece (16), described equipment is characterised in that:

Mapping block (18), comprising:

Generalized Additive Models mapper (32), is configured to utilize Generalized Additive Models, and extracted Feature Mapping is arrived at least one high frequency band parameters

Frequency shifter (34), is configured to low band audio signal

copy frequency displacement to high frequency band;

Envelope control device (36), is configured to control by described at least one high frequency band parameters the envelope of copy after frequency displacement.

10. equipment according to claim 9, wherein, described Generalized Additive Models mapper (32) is configured to make the feature of described mapping based on extracted

sigmoid function and.

11. equipment according to claim 10, wherein, described Generalized Additive Models mapper (32) is configured to carry out mapping according to following formula:

${\hat{E}}_k=w_{0k}+\underset{m=1}{\overset{2}{\mathrm{\&Sigma;}}}\frac{w_{1\mathrm{mk}}}{1+\exp (-w_{2\mathrm{mk}}{F}_m+w_{3\mathrm{mk}})}$

Wherein,

the high frequency band parameters that defines gain, the envelope of K predetermined frequency band of copy after the frequency displacement of this gain control low band audio signal,

{ w _0k, w _1mk, w _2mk, w _3mkthat definition is for each high frequency band parameters

the mapping coefficient set of sigmoid function,

F _m, m=1,2, be the feature of describing the low band audio signal of the energy Ratios between the different piece of low band audio signal frequency spectrum.

12. equipment according to claim 10, wherein, described Generalized Additive Models mapper (32) is configured to carry out mapping according to following formula:

${\hat{E}}_k^C=w_{0k}^C+\underset{m=1}{\overset{2}{\mathrm{\&Sigma;}}}\frac{w_{1\mathrm{mk}}^C}{1+\exp (-w_{2\mathrm{mk}}^C{F}_m+w_{3\mathrm{mk}}^C)}$

Wherein,

high frequency band parameters, the gain that the definition of this high frequency band parameters is associated with signal classification C, and control the envelope of K predetermined frequency band of copy after the frequency displacement of low band audio signal, wherein, described signal is classified C to by low band audio signal

the source sound signal representing is classified,

that definition is for each high frequency band parameters in signal classification C

the mapping coefficient set of sigmoid function,

F _m, m=1,2, be the feature of describing the low band audio signal of the energy Ratios between the different piece of low band audio signal frequency spectrum.

13. according to the equipment described in claim 11 or 12, and wherein, described feature extraction piece (16) is configured to extract feature F by following formula ₁:

$F_1=\frac{E_{10.0-11.6}}{E_{8.0-11.6}}$

Wherein,

E _10.0-11.6the estimation of the energy in frequency band 10.0-11.6kHz to low band audio signal,

E _8.0-11.6it is the estimation of the energy in frequency band 8.0-11.6kHz to low band audio signal.

14. according to the equipment described in claim 11 or 12, and wherein, described feature extraction piece (16) is configured to extract feature F by following formula ₂:

$F_2=\frac{E_{8.0-11.6}}{E_{0.0-11.6}}$

Wherein,

E _8.0-11.6the estimation of the energy in frequency band 8.0-11.6kHz to low band audio signal,

E _0.0-11.6it is the estimation of the energy in frequency band 0.0-11.6kHz to low band audio signal.

15. according to the equipment described in claim 11 or 12, and wherein, described Generalized Additive Models mapper (32) is configured to extracted Feature Mapping to K=4 high frequency band parameters

16. equipment according to claim 12, comprise mapping coefficient Resource selection device (38), and it is configured to select the mapping coefficient set corresponding with signal classification C

wherein C is provided by following formula:

Wherein,

the estimation of the energy in frequency band 8.0-11.6kHz to source sound signal, and

it is the estimation of the energy in frequency band 11.6-16.0kHz to source sound signal.

17. 1 kinds of Voice decoders, comprise according to the equipment (30) described in any one in aforementioned claim 9 to 16.

18. 1 kinds of network nodes, comprise Voice decoder according to claim 17.

19. network nodes according to claim 18, wherein, described network node is wireless terminal.

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